Statistical Dental Prosthesis Design Without Hidden Surface Data
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Solution Overview
Problem
Existing methods for designing dental prostheses struggle with accurately determining the preparation surface, especially when parts of the preparation surface are hidden or obscured by foreign objects like gingiva, leading to inaccurate and cumbersome processes.
Innovation Solution
A method that uses a statistical design model, such as a trained machine-learning model or neural network, to predict the surface of a dental prosthesis without requiring knowledge or estimation of hidden parts of the preparation surface, utilizing a digital 3D representation and incorporating data sets of known preparation surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Shape
If the preparation line is located sub-gingivally to achieve an aesthetically pleasing result, then the aesthetic appearance is improved, but the preparation line becomes hidden by the gingiva, making it invisible in the digital 3D model
Solution Approach 1:
The patent creates a digital 3D model (copy) of the tooth preparation and surrounding structures. The system processes this digital copy to identify and mark the preparation line even when it's not directly visible in the scan, thereby preserving the aesthetic sub-gingival positioning while recovering the lost information about the preparation line location through computational analysis of the digital model
Solution Approach 2:
The patent replaces traditional mechanical methods of preparing and marking the preparation line with a computational approach. Instead of physically exposing the preparation line through gingival retraction, the system uses image processing algorithms to detect and mark the preparation line in the digital 3D model, substituting mechanical intervention with computational analysis
2Loss of information
If retraction cord is inserted to expose the preparation surface for scanning, then the visibility of the preparation line is improved, but the procedure becomes unpleasant to the patient and may cause gingiva collapse
Solution Approach 1:
The patent replaces the mechanical retraction cord procedure with a computational image processing system. The algorithm analyzes the digital 3D model to identify the preparation line without requiring physical manipulation of the gingiva, thereby eliminating patient discomfort and preventing gingival collapse while still achieving visibility of the preparation line in the digital model
Solution Approach 2:
The patent introduces an intermediary computational process (image processing algorithm) between the digital scan and the final preparation line identification. This intermediary system processes the scan data to detect the preparation line indirectly, avoiding the need for direct physical exposure through retraction cords and their associated harmful effects
3Manufacturing precision
If traditional physical methods are used to remove material from gypsum cast, then the preparation surface can be estimated, but the quality depends on the experience of the dental technician and is time-consuming
Solution Approach 1:
The patent replaces manual physical marking and estimation with an automated computational algorithm. The system uses image processing to automatically detect and mark the preparation line in the digital 3D model, eliminating the need for experienced dental technicians to manually estimate the preparation line on gypsum casts, thereby achieving consistent accuracy without manual intervention and significantly reducing the time required
Solution Approach 2:
The system performs self-service by automatically analyzing the digital 3D model and identifying the preparation line without requiring manual intervention. The algorithm independently processes the scan data, detects features, and marks the preparation line, making the process autonomous and eliminating dependency on technician experience and manual time investment
Data Source
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Figure 2A~2B
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AI summary
The present disclosure relates to a computer-implemented method for designing a dental prosthesis. A first step of the method may be to obtain a digital 3D representation of a surface, the surface comprising at least a part of a preparation surface adapted to receive the dental prosthesis, wherein the preparation surface comprises a visible part and a hidden part. A second step of the method may be to determine at least one part of a surface of the dental prosthesis using a statistical design model, wherein the at least one part is determined independent of any knowledge or estimate of the hidden part of the preparation surface. A final step of the method may be to design the dental prosthesis wherein the surface of the dental prosthesis comprises the at least one part. The present disclosure further relates to a system comprising a 3D scanning device and a data processing system for carrying out the steps of the described method.